Elastic computer power and storage provided by a cloud infrastructure may be attractive but it is still limited by poor communication performance and lack of support in using GPGPUs within a virtual machine instance. The GPU Virtualization Service (gVirtuS) presented in this work tries to fill the gap between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for-use high performance virtual clusters deployed via public or private computing clouds. gVirtuS allows an instanced virtual machine to access GPGPUs in a transparent way, with an overhead slightly greater than a real machine/GPGPU setup. gVirtuS is hypervisor independent, and, even though it currently virtualizes nVIDIA CUDA based GPUs, it is not limited to a specific brand technology.

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Industry Perspectives

Over at the IBM Blog, IBM Fellow Hillary Hunter writes that the company anticipates that the world’s volume of digital data will exceed 44 zettabytes, an astounding number. "IBM has worked to build the industry’s most complete data science platform. Integrated with NVIDIA GPUs and software designed specifically for AI and the most data-intensive workloads, IBM has infused AI into offerings that clients can access regardless of their deployment model. Today, we take the next step in that journey in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new data science toolkit, RAPIDS, across our portfolio so that our clients can enhance the performance of machine learning and data analytics." [Read More...]

White Papers

This pioneering study focuses primarily on the virtual performance of throughput workloads. Download the new white paper from VMWare that explores the possibilities of virtualizing HPC throughput in computing environments.